CricPredict: resource-aware prediction of T20 cricket match
One of the key problems in cricket is the increasing number of abandoned matches due to unusual circumstances. There is a total of three different formats in cricket e.g., Test, ODI and T20 international. Usually, the Duckworth–Lewis (D/L) method is used to decide the outcome of the match in Test an...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/9739/45/1571037982.pdf |
| _version_ | 1824446492127002624 |
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| author | Kumar, Ashish Hassan, Bilal Wasiq, Muhammad Farooq |
| author_facet | Kumar, Ashish Hassan, Bilal Wasiq, Muhammad Farooq |
| author_sort | Kumar, Ashish |
| collection | LMU |
| description | One of the key problems in cricket is the increasing number of abandoned matches due to unusual circumstances. There is a total of three different formats in cricket e.g., Test, ODI and T20 international. Usually, the Duckworth–Lewis (D/L) method is used to decide the outcome of the match in Test and ODI cricket, resulting in favour of one team like completed matches. In contrast to the traditional D/L method, we tried to incorporate players' performance indicators into our proposed architecture despite the traditional D/L method which only includes the current state of the match and determines the outcome. To accomplish this task, we tried multiple different machine learning techniques e.g., Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve-Bayes, Linear Regression and Polynomial Regression and a deep learning model to predict the outcome of the match. To train and validate our developed architecture, we crawled data from the Indian Premier League (IPL) for the completed matches. Our proposed architecture takes complete matches as input and for the second batter, it predicts outcome at intermediate stages of matches. Later, the performance of our proposed architecture is computed using different performance indicators e.g., accuracy, Mean squared error etc. In our opinion, our proposed resource-aware prediction architecture is a unique contribution of its kind in the field. |
| first_indexed | 2025-02-19T01:16:01Z |
| format | Conference or Workshop Item |
| id | oai:repository.londonmet.ac.uk:9739 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2025-02-19T01:16:01Z |
| publishDate | 2024 |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:97392025-01-02T13:21:34Z https://repository.londonmet.ac.uk/9739/ CricPredict: resource-aware prediction of T20 cricket match Kumar, Ashish Hassan, Bilal Wasiq, Muhammad Farooq 000 Computer science, information & general works 050 General serial publications 790 Recreational & performing arts One of the key problems in cricket is the increasing number of abandoned matches due to unusual circumstances. There is a total of three different formats in cricket e.g., Test, ODI and T20 international. Usually, the Duckworth–Lewis (D/L) method is used to decide the outcome of the match in Test and ODI cricket, resulting in favour of one team like completed matches. In contrast to the traditional D/L method, we tried to incorporate players' performance indicators into our proposed architecture despite the traditional D/L method which only includes the current state of the match and determines the outcome. To accomplish this task, we tried multiple different machine learning techniques e.g., Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve-Bayes, Linear Regression and Polynomial Regression and a deep learning model to predict the outcome of the match. To train and validate our developed architecture, we crawled data from the Indian Premier League (IPL) for the completed matches. Our proposed architecture takes complete matches as input and for the second batter, it predicts outcome at intermediate stages of matches. Later, the performance of our proposed architecture is computed using different performance indicators e.g., accuracy, Mean squared error etc. In our opinion, our proposed resource-aware prediction architecture is a unique contribution of its kind in the field. 2024-09-25 Conference or Workshop Item PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/9739/45/1571037982.pdf Kumar, Ashish, Hassan, Bilal and Wasiq, Muhammad Farooq (2024) CricPredict: resource-aware prediction of T20 cricket match. In: 2024 International Conference on Electrical Engineering and Computer Science (ICECOS 2024), 25-26 September 2024, Palembang, Indonesia. https://doi.org/10.1109/icecos63900.2024.10791075 10.1109/icecos63900.2024.10791075 10.1109/icecos63900.2024.10791075 |
| spellingShingle | 000 Computer science, information & general works 050 General serial publications 790 Recreational & performing arts Kumar, Ashish Hassan, Bilal Wasiq, Muhammad Farooq CricPredict: resource-aware prediction of T20 cricket match |
| title | CricPredict: resource-aware prediction of T20 cricket match |
| title_full | CricPredict: resource-aware prediction of T20 cricket match |
| title_fullStr | CricPredict: resource-aware prediction of T20 cricket match |
| title_full_unstemmed | CricPredict: resource-aware prediction of T20 cricket match |
| title_short | CricPredict: resource-aware prediction of T20 cricket match |
| title_sort | cricpredict resource aware prediction of t20 cricket match |
| topic | 000 Computer science, information & general works 050 General serial publications 790 Recreational & performing arts |
| url | https://repository.londonmet.ac.uk/9739/45/1571037982.pdf |
| work_keys_str_mv | AT kumarashish cricpredictresourceawarepredictionoft20cricketmatch AT hassanbilal cricpredictresourceawarepredictionoft20cricketmatch AT wasiqmuhammadfarooq cricpredictresourceawarepredictionoft20cricketmatch |